News is still structured as prose — created for a reader, not a database. Compressed Information Expression changes this: a formal specification language for discrete, observable events, built for systematic, structured analysis at the scale the world now demands.
Every entry below is a real event, encoded in Compressed Information Expression.
The systems through which political events are recorded and analyzed were built for a world of information scarcity. They have not been updated for a world of information volume. Every layer introduces distortion — and the distortions compound.
The volume of political reporting now produced globally every hour exceeds what any system can responsibly process. Yet the fundamental architecture — the article, the wire item — has not changed. Accumulation is not analytical progress.
A news story embeds discrete events alongside quotes, commentary, background, and speculation — unlabeled and undifferentiated. Amid the need to fill news space, less than 30% of front-page stories often qualify as new events. The rest is context and noise.
"Tensions rise." "Escalates his feud." These are not descriptions of observable events — they are editorial interpretations embedded in the grammatical form of fact. Any system trained on political prose inherits these frames invisibly.
Every organization that tries to feed political events into a model, a trading system, or a back-test faces the same preprocessing burden: entity resolution, deduplication, normalization. Weeks of work. No methodological consistency. Repeated from scratch each time.
Important events are reported first — and sometimes exclusively — in languages other than English. Automated translation approximates meaning. Political terminology, formal titles, and legal concepts are among the first casualties. A language-independent encoding layer is the only solution.
LLMs process political prose fluently and inherit all of its failures at scale. Recent studies put AI error rates on generated political information at up to 55%. Mondium embeds a structured encoding layer between the raw information and the analytical system — human for fidelity, machine-parseable for scale.
CIE is a domain-specific encoding language for discrete, observable political events. Human-authored for fidelity. Machine-parseable for scale. Governed by a formal specification for consistency across analysts, time, and geography.
ind.hog <[mt]> - rus.hos, rus.min.dfns.exc.01 @ ind.hps - <{sp}> $ eng.hyc, eng.nuc - rus.hos > @ *.loc ∫ 04122025:05122025
The capabilities below are not independent features. Each one is built on the structured event corpus that precedes it. None of them can be replicated by systems that begin with unstructured prose.
Query the event corpus directly — by actor, action type, domain, location, or time window — across jurisdictions and source languages, without normalization overhead.
Learn more →Prediction markets built on structured inputs — with event-level attribution for every probability movement, competing models, independent analyst weighting, and full backtesting capability.
Learn more →Reports generated from the encoded event corpus — not from scraped prose. Every claim traces to a specific event and source record. Explicit assumptions. No black box.
Learn more →Analytical intelligence about the analysis itself — why analysts diverged, who moved toward accuracy and why, what the variance between competing models reveals about the events.
Learn more →Cypher is not yet in public release. But we are interested in contact from analysts, researchers, risk professionals, and technologists who have felt firsthand the limitations of the tools currently available — and who recognize what a structured encoding layer makes possible.